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Artificial Intelligence
Introduction to Artificial Intelligence
1. What is AI
A question that has always been prevalent in the field of computer science is whether a computer
can think. Can a machine emulate a human and can it ever display human intelligence? If so, how
can we create such a machine?
Thus, beyond the calculations and the information processing that computers are expected to do,
an enigmatic branch of computer science called artificial intelligence was born.
Below is the definition given by the Online Dictionary of Computing:
[Artificial intelligence is] the subfield of computer science concerned with the
concepts and methods of symbolic inference by computer and symbolic knowledge
representation for use in making inferences. AI can be seen as an attempt to
model aspects of human thought on computers. It is also sometimes defined as
trying to solve by computer any problem that a human can solve faster.
The purpose of AI is often cited as an attempt to model human thought on computers, that is, to
make machines exhibit behavioral and thinking patterns that are similar to humans.
Some important questions:
1. How do we define human behavioral and thinking patterns?
2. How do we define a rational behavior?
3. What should be the difference between an ordinary program (say a sorting program) and an
AI program?
4. What is the essence of intelligent behavior?
Strong AI: to create "a robot which is autonomous, thinks for itself, makes its own
decisions, can deliberate about its own thoughts, can learn and adapt to new situations
and can communicate with humans through the use of language."
The term was introduced by Searle related to the "Chinese room argument"
http://www.iep.utm.edu/chineser/
Weak AI: To build a computer program that simulates a particular intelligent activity, e.g.
playing chess
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2. Intelligent agents
Features
Goal-motivated behavior
Decision making
Knowledge-based (guided) behavior
Adaptive behavior
Interaction and communication
Physical activity
Topics
State space search
Logical systems; reasoning patterns
Knowledge representation
Information
Data
Knowledge
Machine learning
Vision, Pattern recognition
Natural Language Processing
Robotics
Specifics of AI problems
Require both procedural and declarative knowledge
Require reasoning abilities
Require planning
Require learning abilities
AI Landscape

AI paradigms:
Symbolic AI
Connectionist AI

AI methods
Knowledge representation
Semantic networks
Frames
Production rules
Reasoning
Predicate logic
Probabilistic reasoning,
Modal logic
Common sense reasoning – the CYC project
State space search
State space: Initial state Goal state, intermediate states.
Task - find a path between the initial and the goal states

AI problems
Planning, Decision making
Pattern recognition, Vision
Machine learning
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
Intelligent Agents
Expert Systems
Games
Robots
Softbots
3. Foundations of AI
Philosophy
428 BC : birth of Plato
Aristotle (student of Plato) tried to formulate more precisely the laws governing the
rational part of the mind.
Developed a system of syllogisms, which in principle allows to mechanically generate
conclusions, given initial premises..
The big philosophical question - relation between mind and matter
Mathematics
Logic became a mathematical subject when George Boole in 1847 introduces a formal
language for making logical inferences.
Psychology/ Cognitive psychology / Cognitive Science
How do we learn things about the world?
Concerned with knowledge acquiring and representation in the human mind
Computer Engineering
Hardware suitable for modeling thought processes
Sequential processing - not adequate
Parallel and distributed processing
Connectionist models
Linguistics/ Computational Linguistics/ Natural language processing
Obvious feature of intelligent entities - language use.
Understanding language requires understanding subject matter and context.
4. History of AI
1923
Karel Capek's play "R.U.R." (Rossum's Universal Robots) opens in London (1923). - First use of
the word 'robot' in English.
1943
Warren McCulloch and Walter Pitts: (1943) First work generally recognized as AI
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Proposed a model of connected artificial neurons, capable of computing any computable
function, and capable of learning.
Used 3000 vacuum tubes to simulate a network of 40 neurons.
1950
Turing Test: A. M. Turing Computing Machinery and Intelligence Mind 49: 433-460
http://www.abelard.org/turpap/turpap.php
http://archive.computerhistory.org/projects/chess/related_materials/text/2-0%20and%2021.Computing_machinery_and_intelligence.turing/2-0%20and%2021.Computing_machinery_and_intelligence.turing-alan.mind-59.1950.062303001.pdf
Isaac Asimov (http://www.asimovonline.com/ ) published his three laws of robotics
1956
Dartmouth workshop 1956.
Alan Turing, John McCarthy, Marvin Minsky, Alan Newell
Proposed the name ARTIFICIAL INTELLIGENCE
Dartmouth proposal: http://www.aaai.org/ojs/index.php/aimagazine/article/view/1904/1802
Logic Theorist - a reasoning program to prove theorems. (Newell and Simon)
1958
LISP (McCarthy) the second oldest language in current use
Solving problems in microworlds: e.g. the block world
1965
ELIZA by Joseph Weinzenbaum (MIT), an interactive program that carries on a dialogue in
English on any topic
http://www.parnasse.com/drwww.shtml
http://www-ai.ijs.si/eliza/eliza.html
Work on machine translation - unsuccessful.
1966
Ross Quillian (PhD dissertation, Carnegie Inst. of Technology; now CMU) demonstrated
semantic nets.
Negative report on machine translation kills much work in Natural Language Processing (NLP)
for many years. Difficulties: Lack of knowledge about world. The scale problem.
1969-1979
Knowledge -based systems
DENDRAL (Buchanan et al, 1969) - inferring molecular structure from the information
provided by a mass spectrometer.
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SHRDLU Terry Winograd - understanding natural language in the block world
http://hci.stanford.edu/~winograd/shrdlu/
http://hci.stanford.edu/winograd/
1975
Knowledge representation - frames. Marvin Minsky
http://en.wikipedia.org/wiki/Marvin_Minsky
http://web.media.mit.edu/~minsky/
1980
R1 - first commercial ES – Drew McDermott 1982, configures orders for new computer systems.
http://cs-www.cs.yale.edu/homes/dvm/
http://en.wikipedia.org/wiki/Drew_McDermott
1984
Douglas Lenat (http://en.wikipedia.org/wiki/Douglas_Lenat started the CYC project with the
goal to create a comprehensive ontology and a knowledge base of everyday common sense
knowledge. http://www.cyc.com/
1981
Fifth generation project in Japan - a ten year plan to build intelligent computers running Prolog
1986
Return of neural networks
Parallel distributed processing, connectionist models
1987
Marvin Minsky : The Society of Mind, a theoretical description of the mind as a collection of
cooperating agents
1990’s
Major advances in all areas of AI, with significant demonstrations in machine learning, intelligent
tutoring, case-based reasoning, multi-agent planning, scheduling, uncertain reasoning, data
mining, natural language understanding and translation, vision, virtual reality, games, and other
topics.
2000’s
Robotics – interactive robot pets, NASA robots, military applications, social robots.
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Military application:
http://www.youtube.com/watch?v=mpBG-nSRcrQ
http://www.youtube.com/watch?v=nUQsRPJ1dYw
Playing soccer: http://www.youtube.com/watch?v=XLKKbz2mNyo
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